Object recognition is a technology in the field of computer vision for finding and identifying objects in an image or video sequence. Typically, an object recognition model is a machine learning model related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
Convolutional Neural Network (CNN) is a type of premier algorithm used in the object recognition. A standard CNN consist of a series of layers that perform mathematical computations on an image. The recognizing and classifying of objects into fine grained categories requires a deep CNN with many layers. Each layer requires millions of floating point operations, and also requires memory access by corresponding Central Processing Unit (CPU).
Anomaly detection is an unsupervised learning task where the goal is to identify abnormal patterns or motions in video data that are by definition, infrequent or rare events. However, a key aspect of an anomaly is not just that it is rare, but that it breaks the pattern. Negative anomalies are situations in which something fails to happen, which would have normally have happened. An existing anomaly detection system detects visual anomalies in video streams by first implementing a series of convolutional layers to produce a feature representation of the visual input, and then clustering each representation in one or more clusters in such a way that objects in the same cluster are more similar to each other than to those in other clusters. If a frame appears outside of a cluster, it is considered an anomaly.
However, existing anomaly detection systems are only good at detecting out of place objects, for example, a car driving on a pedestrian path. They do not consider long-term patterns, for example, the changing of the guard at Buckingham Palace occurs at a regular time each day and an existing anomaly detection system may consider the red coated guards an anomaly every day, because they do not look like most tourists. Further, existing anomaly detection systems do not detect negative anomalies. Also, such systems produce images with highlights of the anomalous object, but do not provide text labels that identify the object.
In view of the above, there is a need for an anomaly detection technique in object recognition systems, that is less computationally complex and has increased speed and accuracy. The anomaly detection technique should be able to detect negative anomalies, and provide text labels that identify the anomalous objects. Such object recognition system should allow for smooth object-recognition output on less powerful hardware such as edge devices and small computers that lack Graphic processing units (GPUs), so as to save computational resources and electricity costs, and therefore achieve longer operating time, especially on battery operated portable devices.